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A GAUSSIAN DERIVATIVE OPERATOR FOR AUTHENTIC EDGE DETECTION AND ACCURATE EDGE LOCALIZATION

机译:高斯微分算子,用于真实边缘检测和精确边缘定位

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One of the nice properties of the Gaussian scale space map is its well behavedness. This rather well-behaved nature is somewhat deceptive, however, as portions of the map may not have any direct relationship to the features in the unfiltered image ~4 It has been shown that not all zero-crossing surface patches can be associated with intensity changes in the unfiltered image. Zero-crossings give rise to both authentic and phantom scale map contours. Recently, we proposed an edge enhancement operator, the LWF, which is a weighted combination of the Gaussian and its second derivative. ~6 In this paper, we prove analytically and demonstrate experimentally that the LWF produces the authentic scale map contours only. We also show that the LWF has excellent edge localization (i.e. the points marked by the operator is very close to center of the true edge). A performance comparison between the Laplacian of Gaussian and LWF operators with respect to the localization property is also presented.
机译:高斯比例尺空间图的好特性之一是其良好的行为。但是,由于地图的某些部分可能与未经过滤的图像中的特征没有任何直接关系,因此这种表现良好的性质具有一定的欺骗性〜4已经显示出并非所有的零交叉表面斑块都可以与强度变化相关联。在未过滤的图像中。过零会产生真实和幻像比例尺的地图轮廓。最近,我们提出了一种边缘增强算子LWF,它是高斯及其二阶导数的加权组合。 〜6在本文中,我们通过分析证明并通过实验证明LWF仅生成真实的比例尺地图轮廓。我们还显示LWF具有出色的边缘定位能力(即,操作员标记的点非常接近真实边缘的中心)。还介绍了高斯的拉普拉斯算子和LWF算子在定位特性方面的性能比较。

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